Multiview Centroid Based Fuzzy Classification of Large Data

Modern data is increasingly complex. High dimensionality, heterogeneity and independent multiple representations are the basic properties of today's data. With increasing sources of data collection, a single object can have multiple representations, which we call views. In this paper we propose a multiview classification technique, which uses fuzzy mapping to obtain maximum similarity between an object and nearest multiview centroids. Our fuzzy mapping based approach obtains a unit L1 hyperplane as a common space for each view. To establish the efficacy of our proposed method we present experimental comparisons with number of baselines on two synthetic and two real-world data sets.